Predictive capability of emerging technologies for construction management control: A bibliometric analysis

Abstract

This research presents an in-depth thematic study on the influence of emerging technologies on management control through the predictive capacity deployed in the context of construction costs and deadlines. Using a rigorous bibliometric approach implemented on a set of 91 scientific articles listed in Scopus covering a period of 2016-2025, it offers a cartographic assessment of the evolution of this field of study and its dominant themes. The results show an overwhelming dominance (25.5% of the themes) of artificial intelligence and machine learning techniques focused on the prediction of cost overruns and deadline risks, while advanced meta-ensemble models, deep neural networks (LSTM) and neuro-fuzzy systems (ANFIS) outperform 85% in predicting the achievement of budgetary objectives, thus revolutionizing traditional budgetary practices. However, we observe an existential crisis: too much unity on the existing economic situation and no research on promising technologies such as advanced IoT, digital twins or blockchain, while AI/ML (31.9%) is mobilized at a rate of 25.5% of study/evaluation projects, compared to 17% for construction program management, 10.6% for risk management and 10.6% for value management or feature modeling, a serious imbalance that this study verifies in the light of research in project management, subject to field testing, even if the research is interdisciplinary and, above all, adapted to the specificities of construction project management. The study concludes on the need to broaden the scope of future technological research to, at equivalent quality, rebalance costs and deadlines in the development of necessarily integrated approaches in construction project management

Classification JEL : M41, M15, L74, O32, C45, C53

Paper type : Theoretical Research